int LINEAR::recognize( const ColumnVector &v ) const {
int label;
if( v.length() < liblinear->prob.n ){
ERR_PRINT("Dimension of feature vector is too small. %d < %d\n",v.length(),liblinear->prob.n );
exit(1);
}
else if( v.length() > liblinear->prob.n )
ERR_PRINT("Warning: Dimension of feature vector is too large. %d > %d\n",v.length(),liblinear->prob.n );
// 特徴ベクトルをセット
struct feature_node *x = new struct feature_node[ liblinear->prob.n + 2 ];
int idx = 0;
for( int i = 0; i < liblinear->prob.n; i++ ){
x[ idx ].index = i;
x[ idx ].value = v( i );
if( is_scaling ){
// 下記の条件を満たさないときはスキップ（idxを更新しない）
if( ( feature_max[ x[ idx ].index ] != feature_min[ x[ idx ].index ] ) && ( x[ idx ].value != 0 ) ){
x[ idx ].value = scaling( x[ idx ].index, x[ idx ].value );
if( liblinear->model->bias < 0 ) x[ idx ].index++; // indexが１から始まる
++idx;
}
}
else{
// 下記の条件を満たさないときはスキップ（idxを更新しない）
if( x[ idx ].value != 0 ){
if( liblinear->model->bias < 0 ) x[ idx ].index++; // indexが１から始まる
++idx;
}
}
}
if(liblinear->model->bias>=0){
x[ idx ].index = liblinear->prob.n;
x[ idx ].value = liblinear->model->bias;
idx++;
}
x[ idx ].index = -1;
// predict
if( probability ){
if( liblinear->model->param.solver_type != L2R_LR ){
ERR_PRINT( "probability output is only supported for logistic regression\n" );
exit(1);
}
label = predict_probability(liblinear->model,x,prob_estimates);
// for(j=0;j<nclass;j++)
// printf(" %g",prob_estimates[j]);
// printf("\n");
}
else
label = predict(liblinear->model,x);
if( is_y_scaling )
label = y_scaling( label );
delete x;
return label;
}
/* Solve Ax = b using the Jacobi Method. */
Matrix jacobi(Matrix& A, Matrix& b)
{
ColumnVector x0(A.rows()); // our initial guess
ColumnVector x1(A.rows()); // our next guess
// STEP 1: Choose an initial guess
fill(x0.begin(),x0.end(),1);
// STEP 2: While convergence is not reached, iterate.
ColumnVector r = static_cast<ColumnVector>(A*x0 - b);
while (r.length() > 1)
{
for (int i=0;i<A.cols();i++)
{
double sum = 0;
for (int j=0;j<A.cols();j++)
{
if (j==i) continue;
sum = sum + A(i,j) * x0(j,0);
}
x1(i,0) = (b(i,0) - sum) / A(i,i);
}
x0 = x1;
r = static_cast<ColumnVector>(A*x0 - b);
}
shared_ptr<Matrix> final_x(new Matrix(static_cast<Matrix>(x0)));
return *final_x;
}
/* solve Ax=b using the Method of Steepest Descent. */
Matrix steepestDescent(Matrix& A, Matrix& b)
{
// the Method of Steepest Descent *requires* a symmetric matrix.
if (isSymmetric(A)==false)
{
shared_ptr<Matrix> nullMat(new Matrix(0,0));
return *nullMat;
}
/* STEP 1: Start with a guess. Our guess is all ones. */
ColumnVector x(A.cols());
fill(x.begin(),x.end(),1);
/* This is NOT an infinite loop. There's a break statement inside. */
while(true)
{
/* STEP 2: Calculate the residual r_0 = b - Ax_0 */
ColumnVector r = static_cast<ColumnVector> (b - A*x);
if (r.length() < .01) break;
/* STEP 3: Calculate alpha */
double alpha = (r.transpose() * r)(0,0) / (r.transpose() * A * r)(0,0);
/* STEP 4: Calculate new X_1 where X_1 = X_0 + alpha*r_0 */
x = x + alpha * r;
}
shared_ptr<Matrix> final_x(new Matrix(static_cast<Matrix>(x)));
return *final_x;
}
